The field of event attribution aims to quantify the role that human activities have played in recent climate extreme events. This is done by comparing climate model simulations of the past climate with climate model simulations of a hypothetic counterfactual world assuming humans would not have modified the climate (for example through the emission of greenhouse gases). Researchers then estimate the probability of extreme climate events similar to the event that has occurred in the real-world has changed in these model simulations.

As an example, a seminal study investigating the European heatwave in 2003 found that the likelihood of this event was at least doubled as a consequence of anthropogenic greenhouse emissions. Unfortunately, climate models used for attribution studies are not perfect. Therefore the quantitative estimates they provide are sometimes not consistent with the climate of the real world.

In this study, Centre of Excellence researchers and colleagues identified that climate model ensembles commonly used for event attribution studies show systematic biases in the probability with which they simulate extreme climate events. As a consequence, the quantification of the role of humans in modifying the likelihood that extreme climate events will occur is, in many cases, not meaningful.

This new work published in Nature Communications develops a correction method that ensures the probability of climate extremes in the model simulations are consistent with real-world observations. In addition, it also corrects the rate of the long-term changes and the inter-annual variability so that it is consistent with observations.

This newly developed correction method is a major advance towards providing quantifications of climate change that are more meaningful to the real world.

While the researchers still identify substantial anthropogenic contributions to extreme weather events like heatwaves, the calibration ensures the results are more trustworthy. This correction enables the climate science community to provide more accurate information about how climate change affects extreme events to policy makers or other planners working on implement adaptation measures in a changing climate.